Coding

(in Stata and R)

Ben Harrap

2025-02-14

Overview

In this session we’ll cover:

R fundamentals
Common coding tasks
- Concept
- Data
- Code (Stata and R)

R fundamentals

R and RStudio

R is the language that the code is written in.

RStudio is the software many people use to write R code in.

At a minimum you need to install R (r-project.org)

IDE options include:

RStudio (posit.co/products/open-source/rstudio)
Positron (positron.posit.co)
VS Code (code.visualstudio.com)

A screenshot of the RStudio IDE. Source: Posit

Packages

One of the greatest strengths of R is that it is open-source and there are an enormous number of packages available.

A package is a collection of functions usually written around a particular goal or task.

Packages I recommend:

tidyverse which includes:
- dplyr for data manipulation
- ggplot2 for data visualisation
- stringr for working with text data
- lubridate for working with dates
janitor which has many useful cleaning tools
haven for reading Stata files

Packages

First you need to install packages:

install.packages("tidyverse")

Then you need to load them into your session using library():

library(tidyverse) # Imports lots of useful packages
library(haven) # For reading Stata files (and SPSS and more)
library(janitor) # Handy cleaning functions
library(gt) # For nice tables

Assignment

In Stata, you load one dataset and all commands are executed in relation to the currently loaded dataset.

In R, the default is for commands to output their result in the console.

This means you need to assign the output of your commands to something if you want to store it.

Assignment

If we read in a dataset without assigning anything, it just gets displayed:

read_dta("data/auto.dta")

# A tibble: 74 × 12
   make        price   mpg rep78 headroom trunk weight length  turn displacement
   <chr>       <dbl> <dbl> <dbl>    <dbl> <dbl>  <dbl>  <dbl> <dbl>        <dbl>
 1 AMC Concord  4099    22     3      2.5    11   2930    186    40          121
 2 AMC Pacer    4749    17     3      3      11   3350    173    40          258
 3 AMC Spirit   3799    22    NA      3      12   2640    168    35          121
 4 Buick Cent…  4816    20     3      4.5    16   3250    196    40          196
 5 Buick Elec…  7827    15     4      4      20   4080    222    43          350
 6 Buick LeSa…  5788    18     3      4      21   3670    218    43          231
 7 Buick Opel   4453    26    NA      3      10   2230    170    34          304
 8 Buick Regal  5189    20     3      2      16   3280    200    42          196
 9 Buick Rivi… 10372    16     3      3.5    17   3880    207    43          231
10 Buick Skyl…  4082    19     3      3.5    13   3400    200    42          231
# ℹ 64 more rows
# ℹ 2 more variables: gear_ratio <dbl>, foreign <dbl+lbl>

Instead we have to give it a name and assign to the environment:

data <- read_dta("data/auto.dta")

Multiple datasets

Being able to have many things assigned in the environment at once opens up many possibilities:

Working on multiple datasets simultaneously
- Easier than working with Stata’s frame
Creating subsets of data
- More powerful version of Stata’s preserve and restore
Outputs from multiple analyses are readily available
- Model results, data visualisations

Pipes

In Stata, you write lines of code that get executed one after the other, individually and sequentially.

sysuse auto
drop if foreign == 1
gen wgt_len_ratio = weight/length
gen brand = substr(make, 1, ustrpos(make, " ")-1)
sort brand
by brand: summarize wgt_len_ratio

In R, you can create pipelines using pipes (|>) to link functions together and avoid having to repeatedly refer to the data you want to work with.

Pipes

Without pipes

auto <- read_dta("/Applications/Stata/auto.dta")
auto <- filter(auto, foreign == 0)
auto <- mutate(auto,
               wgt_len_ratio = weight / length,
               brand = str_extract(make, "\\w+"))
summarise(auto,
          n = n(),
          mean = mean(wgt_len_ratio),
          sd = sd(wgt_len_ratio),
          .by = brand)

With pipes

auto <- read_dta("/Applications/Stata/auto.dta")
auto |>
  filter(foreign == 0) |>
  mutate(wgt_len_ratio = weight / length,
         brand = str_extract(make, "\\w+")) |>
  summarise(
    n = n(),
    mean = mean(wgt_len_ratio),
    sd = sd(wgt_len_ratio),
    .by = brand)

First step: Get the data

Reading in a data file

The data you want to work with is contained in a file located somewhere.

To start using the data you need to consider:

The location of the file
- Is the data on your computer? A shared drive? An online location?
- Are you using the absolute or relative pathway?
- How are others going to run your code?
The format of the file (.dta,.xlsx,.csv,.txt,.SAV)

A note on folder structure

Relative vs. absolute directories

Users/Ben/Documents/GitHub/workshops/2025-02-14-coding-in-stata-and-r/data/auto.dta
./data/auto.dta

Network locations, root location different according to machine type.

Generally speaking, avoid using absolute pathways when possible.

Navigate to other directories from the current working directory using .. to go ‘up’ a folder:

../2024-08-20-admin-data/img/aihw.PNG

A note on directories

In Stata you would set your working directory using cd, such as

cd "/Users/ben/Documents/GitHub/workshops/2025-02-14-coding-in-stata-and-r"

In R you can use setwd() to set your working directory

setwd("/Users/ben/Documents/GitHub/workshops/2025-02-14-coding-in-stata-and-r")

I recommend using R Projects instead - note the .Rproj file below. Opening this file will set the directory automatically to the location of that file.

- 2025-02-14-coding-in-stata-and-r
  - _brand.yml
  - 2025-02-14-coding-in-stata-and-r.Rproj
  - slides.html
  - slides.qmd
  - data
  - img

For a file in the same location as the current working directory:

use auto

For a file that is in another folder:

use "data/auto.dta"

For a file that is online:

import delimited "https://raw.githubusercontent.com/allisonhorst/palmerpenguins/refs/heads/main/inst/extdata/penguins_raw.csv"

For a file in the same location as the current working directory:

data <- read_dta("auto.dta")

For a file that is in another folder:

data <- read_dta("data/auto.dta")

For a file that is online:

data <- read_csv("https://raw.githubusercontent.com/allisonhorst/palmerpenguins/refs/heads/main/inst/extdata/penguins_raw.csv")

Overview of the data

Raw data containing observations of three different types of penguins, made available through https://allisonhorst.github.io/palmerpenguins/

gt(data |> slice_sample(n = 10))

studyName	Sample Number	Species	Region	Island	Stage	Individual ID	Clutch Completion	Date Egg	Culmen Length (mm)	Culmen Depth (mm)	Flipper Length (mm)	Body Mass (g)	Sex	Delta 15 N (o/oo)	Delta 13 C (o/oo)	Comments
PAL0910	66	Chinstrap penguin (Pygoscelis antarctica)	Anvers	Dream	Adult, 1 Egg Stage	N99A2	No	2009-11-21	49.6	18.2	193	3775	MALE	9.46180	-24.70615	Nest never observed with full clutch.
PAL0910	104	Gentoo penguin (Pygoscelis papua)	Anvers	Biscoe	Adult, 1 Egg Stage	N23A2	Yes	2009-11-18	49.1	15.0	228	5500	MALE	8.65914	-25.79203	NA
PAL0910	123	Gentoo penguin (Pygoscelis papua)	Anvers	Biscoe	Adult, 1 Egg Stage	N43A1	Yes	2009-11-22	45.2	14.8	212	5200	FEMALE	8.24246	-26.11969	NA
PAL0708	23	Gentoo penguin (Pygoscelis papua)	Anvers	Biscoe	Adult, 1 Egg Stage	N42A1	Yes	2007-11-27	46.5	14.5	213	4400	FEMALE	7.90436	-25.39470	NA
PAL0708	22	Chinstrap penguin (Pygoscelis antarctica)	Anvers	Dream	Adult, 1 Egg Stage	N73A2	Yes	2007-12-03	48.5	17.5	191	3400	MALE	9.42666	-24.26375	NA
PAL0708	25	Gentoo penguin (Pygoscelis papua)	Anvers	Biscoe	Adult, 1 Egg Stage	N44A1	Yes	2007-11-29	42.9	13.1	215	5000	FEMALE	7.68528	-25.39181	NA
PAL0809	80	Gentoo penguin (Pygoscelis papua)	Anvers	Biscoe	Adult, 1 Egg Stage	N60A2	Yes	2008-11-09	45.2	16.4	223	5950	MALE	8.19749	-26.65931	NA
PAL0809	52	Gentoo penguin (Pygoscelis papua)	Anvers	Biscoe	Adult, 1 Egg Stage	N13A2	Yes	2008-11-04	48.5	14.1	220	5300	MALE	8.39867	-26.79358	NA
PAL0708	25	Chinstrap penguin (Pygoscelis antarctica)	Anvers	Dream	Adult, 1 Egg Stage	N89A1	No	2007-11-28	46.7	17.9	195	3300	FEMALE	9.74144	-24.59467	Nest never observed with full clutch.
PAL0809	43	Gentoo penguin (Pygoscelis papua)	Anvers	Biscoe	Adult, 1 Egg Stage	N7A1	Yes	2008-11-04	45.3	13.7	210	4300	FEMALE	8.37615	-26.72791	NA

Second step: Prepare the data

Renaming variables

Variables might be imported with difficult to work with names, so we want to rename them

head(data)

# A tibble: 6 × 17
  studyName `Sample Number` Species          Region Island Stage `Individual ID`
  <chr>               <dbl> <chr>            <chr>  <chr>  <chr> <chr>          
1 PAL0708                 1 Adelie Penguin … Anvers Torge… Adul… N1A1           
2 PAL0708                 2 Adelie Penguin … Anvers Torge… Adul… N1A2           
3 PAL0708                 3 Adelie Penguin … Anvers Torge… Adul… N2A1           
4 PAL0708                 4 Adelie Penguin … Anvers Torge… Adul… N2A2           
5 PAL0708                 5 Adelie Penguin … Anvers Torge… Adul… N3A1           
6 PAL0708                 6 Adelie Penguin … Anvers Torge… Adul… N3A2           
# ℹ 10 more variables: `Clutch Completion` <chr>, `Date Egg` <date>,
#   `Culmen Length (mm)` <dbl>, `Culmen Depth (mm)` <dbl>,
#   `Flipper Length (mm)` <dbl>, `Body Mass (g)` <dbl>, Sex <chr>,
#   `Delta 15 N (o/oo)` <dbl>, `Delta 13 C (o/oo)` <dbl>, Comments <chr>

Stata automatically renames variables that contain spaces and other invalid characters, R does not.

Renaming variables

Stata
R

In Stata, the old name comes first, the new name comes second

rename studyName study_name
rename Species species

Like saying “replace old name with this new name”

In dplyr, the new name comes first, the old name comes second

data |> 
  rename(
    study_name = studyName,
    species = Species
  )

Like saying “use this new name to replace this old name”

Renaming variables

The janitor package contains a handy function called clean_names():

names(data)

 [1] "studyName"           "Sample Number"       "Species"            
 [4] "Region"              "Island"              "Stage"              
 [7] "Individual ID"       "Clutch Completion"   "Date Egg"           
[10] "Culmen Length (mm)"  "Culmen Depth (mm)"   "Flipper Length (mm)"
[13] "Body Mass (g)"       "Sex"                 "Delta 15 N (o/oo)"  
[16] "Delta 13 C (o/oo)"   "Comments"

data <- data |> clean_names()
names(data)

 [1] "study_name"        "sample_number"     "species"          
 [4] "region"            "island"            "stage"            
 [7] "individual_id"     "clutch_completion" "date_egg"         
[10] "culmen_length_mm"  "culmen_depth_mm"   "flipper_length_mm"
[13] "body_mass_g"       "sex"               "delta_15_n_o_oo"  
[16] "delta_13_c_o_oo"   "comments"

Dropping/keeping variables

Stata
R
Bonus R

To keep only the specified variables, we use keep.

keep individual_id date_egg

To drop the specified variables, we use drop.

drop study_name

To keep only the specified variables, we use select() from the dplyr package.

data |> select(individual_id, date_egg)

To drop the specified variables, we add a minus (-) before the name of the variable.

data |> select(-study_name)

We can also select ranges:

data |> select(region:date_egg)

tidyselect provides lots of helpful functions for use in select()!

everything() selects every variable, so the below puts study_name and individual_id first, followed by all other variables in their current order

data |> select(study_name, individual_id, everything())

You can select variables that have names matching a pattern. starts_with() and ends_with() will match the pattern to the start or end of the variable name

matches() looks for it anywhere

data |> select(ends_with("mm"))
data |> select(matches("bill"))

Dropping/keeping observations

Stata
R

Observations (rows) are also dropped using drop.

drop if region == "Anvers"

keep if island == "Biscoe"

drop if samplenumber == .

Rows are are dropped using filter().

data |> filter(region != "Anvers")

data |> filter(island == "Biscoe")

data |> filter(!is.na(samplenumber))

Only rows that meet the condition are kept, use the ‘not’ operator (!) instead of drop and keep.

Note that to drop missing values you must use is.na(), you cannot say samplenumber != NA. NA is a special class in R.

filter() also comes from the dplyr package.

Dropping/keeping observations

Multiple conditions are specified with commas, where all must be true:

data |> 
  filter(
    region != "Anvers",
    island == "Biscoe",
    !is.na(samplenumber)
  )

You can also make use of %in% with lists to make cleaner criteria:

primes <- c(2,3,5,7,11,13,17,19)

data |> filter(sample_number %in% primes)

Creating or modifying variables

Stata
R

Several options in Stata:

gen for making new variables
egen for new variables based on a specific function
replace for modifying existing variables
clonevar for copying existing variables

gen culmen_flipper_ratio = culmen_length_mm / flipper_length_mm
egen weight_third = cut(body_mass_g), group(3)
replace weight_third = -9 if weight_third == .
clonevar weight_category = weight_third

In R, mutate() does everything

data |> mutate(
  culmen_flipper_ratio = culmen_length_mm / flipper_length_mm,
  weight_third = cut(body_mass_g, 3),
  weight_third = ifelse(is.na(weight_third), -9, weight_third),
  weight_category = weight_third
)

Conditions

Stata
R

The if argument is where conditions are typically specified in Stata.

egen weight_third = cut(body_mass_g), group(3)
replace weight_third = -9 if weight_third == .

Common ways of specifying conditions in R:

ifelse(), in base R
if_else() and case_when() from dplyr

data |> mutate(
  # Using ifelse()
  weight_third = ifelse(is.na(body_mass_g), -9, cut(body_mass_g, 3)),
  # Using case_when()
  weight_third = case_when(
    is.na(body_mass_g) ~ -9,
    .default = as.numeric(cut(body_mass_g, 3))
  )
)

Missing

Display

Stata displays missing values in strings as blank "", and uses . in all other cases.

R displays all missing values as NA, meaning the empty string "" is not considered missing.

Interpretation

Stata interprets . as a large positive number, which can cause issues in conditional statements.

# This captures missing values
gen variable = 1 if number > 10
# So missings have to be explicitly excluded
gen variable = 1 if number > 10 & number != .

R does not do this, missing is a separate category.

Variable types

Stata
Integer
Numeric
Logical
Factor

Generally speaking, in Stata everything is either a number or a string.

destring sample_number, replace
tostring sample_number, replace

Numbers can be bytes, integers, decimals, floats, doubles.

Categorical data can be represented as a string or as a label on a numeric variable.

Logical variables (TRUE or FALSE) do not exist.

R has more data types than Stata, including logical variables - click on the other tabs to learn more.

In the example below, values is a vector that’s a mix of numbers and strings (characters). The strings are in quotes, the numbers are not.

values <- c(-4, "0","1", 1,"2.5","05", "11.7", "T", "FALSE")
values

[1] "-4"    "0"     "1"     "1"     "2.5"   "05"    "11.7"  "T"     "FALSE"

as.integer(values)

[1] -4  0  1  1  2  5 11 NA NA

Notice that using as.integer() truncates 2.5 and 11.7, it does not round them.

values

[1] "-4"    "0"     "1"     "1"     "2.5"   "05"    "11.7"  "T"     "FALSE"

as.numeric(values)

[1] -4.0  0.0  1.0  1.0  2.5  5.0 11.7   NA   NA

round(as.numeric(values))

[1] -4  0  1  1  2  5 12 NA NA

Notice that round() rounds decimals of .5 toward the nearest even number .

Use round_half_up() from the janitor package to round up.

values

[1] "-4"    "0"     "1"     "1"     "2.5"   "05"    "11.7"  "T"     "FALSE"

as.logical(values)

[1]    NA    NA    NA    NA    NA    NA    NA  TRUE FALSE

Strings with T/TRUE/F/FALSE are converted to TRUE and FALSE

If we convert them to numbers first, any numbers that are not 0 are now TRUE and 0 is FALSE.

as.logical(as.numeric(values))

[1]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE    NA    NA

The converse is true as well - you can sum logicals as they are treated like 1 and 0

FALSE + TRUE + TRUE + FALSE

[1] 2

Jumping back to the penguins data, categorical data can be stored as factors:

data <- data |> mutate(island_factor = factor(island, levels = c("Dream","Biscoe","Torgersen")))

If we tabulate the character version island, it’s sorted alphabetically

tabyl(data$island)

 data$island   n   percent
      Biscoe 168 0.4883721
       Dream 124 0.3604651
   Torgersen  52 0.1511628

When we use the factor version, the result is ordered by the levels we specified.

tabyl(data$island_factor)

 data$island_factor   n   percent
              Dream 124 0.3604651
             Biscoe 168 0.4883721
          Torgersen  52 0.1511628

Reshaping longer

The tidyverse package makes this very easy with pivot_longer().

data |> 
  select(individual_id, region, stage) |> 
  pivot_longer(
    cols = c(region, stage),
    names_to = "variable",
    values_to = "number"
  )

# A tibble: 688 × 3
   individual_id variable number            
   <chr>         <chr>    <chr>             
 1 N1A1          region   Anvers            
 2 N1A1          stage    Adult, 1 Egg Stage
 3 N1A2          region   Anvers            
 4 N1A2          stage    Adult, 1 Egg Stage
 5 N2A1          region   Anvers            
 6 N2A1          stage    Adult, 1 Egg Stage
 7 N2A2          region   Anvers            
 8 N2A2          stage    Adult, 1 Egg Stage
 9 N3A1          region   Anvers            
10 N3A1          stage    Adult, 1 Egg Stage
# ℹ 678 more rows

cols = specifies which columns you want reshape

names_to = names the column that the variable names will be put in

values_to = names the column that the values of those variables will be put in

Reshaping wider

As you might’ve guessed, pivot_wider() does the opposite

data |> 
  pivot_wider(
    names_from = "island",
    values_from = "sample_number"
  ) |> 
  select(Torgersen, Dream, Biscoe)

# A tibble: 344 × 3
   Torgersen Dream Biscoe
       <dbl> <dbl>  <dbl>
 1         1    NA     NA
 2         2    NA     NA
 3         3    NA     NA
 4         4    NA     NA
 5         5    NA     NA
 6         6    NA     NA
 7         7    NA     NA
 8         8    NA     NA
 9         9    NA     NA
10        10    NA     NA
# ℹ 334 more rows

This time we don’t specify cols

names_from = says which column use for the new column names (Torgersen, Dream, Biscoe)

values_from = says which column to pull the values for those new columns from

Grouped operations

Stata
R

In Stata, the by command is used as a prefix for certain functions

sort species
by species: summarize body_mass_g

The data must be sorted before the by operation can be used.

Grouping can be done in two locations.

As part of a pipe:

data |> 
  group_by(species) |> 
  summarise(
    mean = mean(body_mass_g, na.rm = T),
    sd = sd(body_mass_g, na.rm = T))

Or as part of a function, if it permits:

data |> 
  summarise(
    mean = mean(body_mass_g, na.rm = T),
    sd = sd(body_mass_g, na.rm = T),
    .by = species)

Miscellaneous operations

Seeds

Automating tables

Tabulation options

There are a few good options for making and formatting tables in R:

gt
gtsummary
kable (with kableExtra)
tinytable
flextable

Depending on your output, some packages work better than others. For things like HTML and PDF, they all work fairly well. For Word output (docx), gt doesn’t do so well.

Data visualisation

Analysing data

Reproducible documents

The situation

You are drafting a report or paper using Microsoft Word.

You have:

Four paragraphs with in-text reporting of results
Three tables
Two figures
And a partridge in a pear tree

The problem

Oh no! You now have to update everything because of:

Changes in the data
Changes in the analysis
Changes due to reviewer comments
Changes in the wind

The solution

Use Quarto to write your report/paper and automatically generate the tables, figures, and in-text reporting!

This has several benefits:

Changes to data are automatically incorporated, everywhere
Human error is limited to the initial setup, not every time data needs updating
Updating takes as little as 5 seconds
You learn a cool new skill

What is Quarto?

Quarto is open-source scientific and technical publishing system. It lets you:

Write documents, presentations, and more
Output these documents in Word and PDF
Format text explicitly using markup
Run code within the same document (more on this later)
Manage citations, styles, table and figure numbering
Most importantly, it makes your work more reproducible!

What does Quarto look like?


Text written with Quarto uses markup to apply styling. In this sentence, the word **bold** is bolded and *italic* is italicised.

![A lovely picture of Randy the greyhound](https://benharrap.com/img/randy.png){fig-align="left" width=20%}


You can do all the normal stuff:

1. Make lists
2. Add citations
3. etc.

Text written with Quarto uses markup to apply styling. In this sentence, the word bold is bolded and italic is italicised.

You can do all the normal stuff:

Make lists
Add citations
etc.

What would a workflow look like?

You can create workflow that suits you. I can imagine people:

Doing their analysis in Stata
Creating figures using Stata
Exporting results into an Excel spreadsheet
Reading the spreadsheet into R
Using Quarto for write-up

Data cleaning with Stata
Saving analysis datasets
Importing these datasets into R/Quarto
Creating figures and tables with R
Writing up with Quarto

Doing everything with R/Quarto

A real example

I recently had a paper published: https://doi.org/10.1016/j.anzjph.2025.100249

I did everything we’ve just covered when writing up this paper:

Wrote the whole paper
Automated in-text numbers
Created tables
Included figures created elsewhere
Automated table/figure numbering
Referencing

My entire thesis is like this actually — the code and files are available: https://github.com/benharrap/thesis

A real example

Every Quarto document starts with the parameters:

title: "Potentially preventable hospitalisations for Aboriginal children with experience of out-of-home care: a data linkage study"
author:
  - Benjamin Harrap
  - Alison Gibberd
  - Melissa O'Donnell
  - Koen Simons
  - Sandra Eades
bibliography: "../references.bib"
csl: "../vancouver-superscript.csl"

Then the files get read in

library(tidyverse)
in_text <- read_csv("data/single_stats.csv")
table_1 <- read_csv("data/table_1.csv")

A real example

gt(table_1) |> 
  tab_header("Demographic and other characteristics for the matched cohort of never- and ever-placed children") |> 
  cols_label(
    stat = "",
    never_placed = "Never-placed",
    ever_placed = "Ever-placed"
  ) |> 
  sub_missing(
    everything(),
    missing_text = " "
    )

Demographic and other characteristics for the matched cohort of never- and ever-placed children
	Never-placed	Ever-placed
N	7,442	3,721
IRSAD at birth
1st quintile	3,559 (47.8%)	1,781 (47.9%)
2nd quintile	1,336 (18.0%)	675 (18.1%)
3rd quintile	1,717 (23.1%)	836 (22.5%)
4th quintile	655 (8.8%)	334 (9.0%)
5th quintile	175 (2.4%)	95 (2.6%)
Remoteness area at birth
Major cities	3,802 (51.1%)	1,899 (51.0%)
Inner regional	368 (4.9%)	189 (5.1%)
Outer regional	1,101 (14.8%)	552 (14.8%)
Remote	1,371 (18.4%)	671 (18.0%)
Very remote	800 (10.7%)	410 (11.0%)
All hospital periods
All ages	43,962	29,369
Ages 0-4	22,048 (50.2%)	15,586 (53.1%)
Ages 5-9	12,997 (29.6%)	7,576 (25.8%)
Ages 10-14	8,913 (20.3%)	6,207 (21.1%)
All PPH periods
All ages	7,630	6,491
Ages 0-4	5,366 (70.3%)	4,850 (74.7%)
Ages 5-9	1,752 (23%)	1,227 (18.9%)
Ages 10-14	512 (6.7%)	414 (6.4%)

A real example


While government departments in Australia [@aihw2020b;@dohwa2017;@ahmac2017] and researchers [@li2009;@guthrie2012] report rates of PPHs...


After excluding `ｒ n_missing` who had missing data on the matching variables, a total of `ｒ n_matching` children were included in the matching process. There were `ｒ n_ever` ever-placed children who were matched with `ｒ n_never` never-placed children.


When considering the mechanisms for prevention suggested by Anderson et al. [@anderson2012], most conditions were preventable through early access to primary care.

While government departments in Australia^1–3 and researchers^4,5 report rates of PPHs…

After excluding 125 who had missing data on the matching variables, a total of 33,403 children were included in the matching process. There were 3,721 ever-placed children who were matched with 7,442 never-placed children.

When considering the mechanisms for prevention suggested by Anderson et al.⁶, most conditions were preventable through early access to primary care.

References

Australian Institute of Health and Welfare. Disparities in potentially preventable hospitalisations across Australia, 2012–13 to 2017–18 [Internet]. Canberra: AIHW; 2020 [cited 2024 Jan 15]. Available from: https://www.aihw.gov.au/getmedia/20bc5bf9-d46c-40a7-96c1-d632a1d448bc/aihw-hpf-50.pdf

Department of Health Western Australia. Lessons of Location: Potentially Preventable Hospitalisation Hotspots in Western Australia 2017. Perth; 2017.

Australian Health Ministers’ Advisory Council. Aboriginal and Torres Strait Islander Health Performance Framework 2017 Report [Internet]. Canberra; 2017 [cited 2024 Mar 28]. Available from: https://www.niaa.gov.au/sites/default/files/publications/2017-health-performance-framework-report_1.pdf

Li SQ, Gray NJ, Guthridge SL, Pircher SL. Avoidable hospitalisation in Aboriginal and non-Aboriginal people in the Northern Territory. Medical journal of Australia. 2009;190(10):532–6.

Guthrie J. Estimating the magnitude of potentially avoidable hospitalisations of Indigenous children in the Australian capital territory: Some methodological challenges. Australian Aboriginal Studies (Canberra) [Internet]. 2012;(1):92–7. Available from: https://search.informit.org/doi/10.3316/informit.407306360938004

Anderson P, Craig E, Jackson G, Jackson C. Developing a tool to monitor potentially avoidable and ambulatory care sensitive hospitalisations in New Zealand children. The New Zealand Medical Journal (Online). 2012;125(1366).